Oversizing analysis in plant-wide control design for industrial processes

Zumoffen, David

doi:10.1016/j.compchemeng.2013.03.021

Cited by 18 publications

(53 citation statements)

References 27 publications

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“…Taking into account this variable, the new strategy NC 5 (4 × 4) presents excellent performance when compared against NC 2 (5 × 5): EIP(xme(19)) = 333.02% for idv(1)−idv(4) (scenario 5) and EIP(xme(19)) = 350.41% for idv(8)−idv(12) (scenario 6). (17) xmv(3)−xme (7) xmv(3)−xme (7) xmv(4)−xme (17) xmv(3)−xme(7) xmv(6)−xme (7) xmv(4)−xme (17) xmv(4)−xme (17) xmv(6)−xme (7) xmv(4)−xme(17) xmv(9)−xme (18) xmv (6) Even though NC 2 increases the dimension of the structure, it does not propose a noticeable improvement of the dynamics with respect to NC 5 . This is reflected by the small EIP values of Tables 7, 9, and 10.…”

Section: Application Case: Tennessee Eastmanmentioning

confidence: 99%

“…Following a similar procedure, Zumoffen 18 and Molina et al 19 estimated the normalized steady-state gain matrices G (inputs − outputs) and D (disturbances − outputs). These matrices are utilized in this work for supporting the controller design stage.…”

Section: Application Case: Tennessee Eastmanmentioning

confidence: 99%

“…Stability Behavior. Consider the CV selection proposed by Molina et al 19 for the base case: xme(5), xme(11), xme(13), xme (18), xme (20), xme (17), xme(30), and xme G/H . Taking into account all degrees of freedom (xmv(1) to xmv(11)), the following CV−MV pairing is suggested here: xme(5)−xmv(11), xme(11)−xme(21)sp, xme (13) The first simulation corresponds to a fault in the A feed flow (stream 1) manipulated variable (xmv(3)), called F 1 .…”

Section: Application Case: Tennessee Eastmanmentioning

confidence: 99%

“…It has 12 decision variables for the CVs (corresponding to xme(5), xme(6), xme(9), xme(11), xme(13), xme(16), xme (18), xme (20), xme (7), xme (17), xme(30), and xme G/H ) and 8 decision variables for the MVs (xmv(1), xmv(3), xmv(4), xmv(5), xmv(6), xmv(9), xme(21)sp, and xmv(11)); see Table 1. The components c i cv (1:8) and c i mv (1:8) from eq 9 represent the genes corresponding to xme(5)−xme (20) and xmv(1)−xmv(11), respectively.…”

Section: Application Case: Tennessee Eastmanmentioning

confidence: 99%

See 3 more Smart Citations

Nominal Controller Design Based on Decentralized Integral Controllability in the Framework of Reconfigurable Fault-Tolerant Structures

Luppi

Outbib

Basualdo

2015

Ind. Eng. Chem. Res.

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A reconfigurable fault-tolerant control system typically includes a nominal controller (NC), a fault detection/ diagnosis (FDD) and decision subsystem, a reconfiguration mechanism, and a reconfigurable controller (RC). Here, a systematic methodology for designing a fully decentralized NC of reduced dimension is presented, providing (i) fault-tolerant capability due to the structural flexibility and (ii) availability of redundancies for RC design. The fulfillment of a sufficient condition for decentralized integral controllability is searched to guarantee the stability while the FDD scheme is identifying the faults. A novel framework based on a genetic algorithm is developed for obtaining alternative NCs. They are screened considering quantitative measures derived from stability and performance considerations. The procedure features complexity reduction because (i) it only utilizes steady-state process information and (ii) it is independent from the controller design. The methodology is tested in the Tennessee Eastman process to demonstrate its potential against set point/disturbance changes and stuck actuator faults.

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Section: Application Case: Tennessee Eastmanmentioning

confidence: 99%

Section: Application Case: Tennessee Eastmanmentioning

confidence: 99%

Section: Application Case: Tennessee Eastmanmentioning

confidence: 99%

Section: Application Case: Tennessee Eastmanmentioning

confidence: 99%

See 2 more Smart Citations

Nominal Controller Design Based on Decentralized Integral Controllability in the Framework of Reconfigurable Fault-Tolerant Structures

Luppi

Outbib

Basualdo

2015

Ind. Eng. Chem. Res.

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“…The classical centralized or decentralized control structures are not always the best solution for the multivariable plant‐wide control problem . A systematic and a generalized multivariable plant‐wide control strategy have been proposed . The overall strategy is based on a single multi‐objective combinatorial optimization problem with steady‐state functional costs.…”

Section: Introductionmentioning

confidence: 99%

Model based multivariable control scheme in a reset configuration for stable multivariable systems

Pathiran¹,

Prakash

2017

Can J Chem Eng

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The multivariable control scheme is a widely used advanced process control methodology to control key process variables in chemical engineering processes. However, successful implementation of multivariable control requires a simplified control structure, a lower number of tuning parameters, and an appropriate tuning method. In this work, a model based multivariable control scheme is realized in reset configuration for the control of stable multi‐input and multi‐output systems. This control scheme utilizes the process transfer function matrix and the inverse of the steady state gain matrix for its implementation. The control scheme has a single tuning parameter and also inherently takes into account the interaction that exists between process inputs and outputs. This scheme is easy to implement and apply in multivariable systems with multiple delays, high‐dimensional systems, non‐square systems, and systems with RHP zeros. Simulation examples from process system engineering such as industrial scale polymerization reactor system, quadruple‐tank system, temperature control for the four room process, and shell control problem are presented to show the efficacy of the proposed multivariable control scheme.

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Plant-Wide Control of a Reactive Distillation Column on Biodiesel Production

Regalado-Méndez

Romero

Natividad

et al. 2016

Automation Control Theory Perspectives in Intelligent Systems

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An economical plant-wide control has been designed and implemented for biodiesel production by reactive distillation. Two available degrees of freedom have been assigned, to control the active constraints. The main conclusions were: (1) the optimal operation with lower value of the cost function (−946.72 USD/min) was established with liquid reflux, vapor stream, and feed flow molar ratio Methanol/Triglycerides of 8.43 kmol/min, 4.52 kmol/min, and 333:25, respectively. Additionally, the Biodiesel European Quality Standards were largely satisfied (Total Glycerol 0.25 wt%), even it exists an over purification, (2) the plant-wide control designed presented an adequate performance, maintaining stable and robust to disturbances and set-point changes, as it always reaches requested reference. Finally, in this study, the Tyreus-Luyben tuning method was the best tuning method, as it takes the least of time to reach the steady state.

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Oversizing analysis in plant-wide control design for industrial processes

Cited by 18 publications

References 27 publications

Nominal Controller Design Based on Decentralized Integral Controllability in the Framework of Reconfigurable Fault-Tolerant Structures

Nominal Controller Design Based on Decentralized Integral Controllability in the Framework of Reconfigurable Fault-Tolerant Structures

Model based multivariable control scheme in a reset configuration for stable multivariable systems

Plant-Wide Control of a Reactive Distillation Column on Biodiesel Production

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